Alleviating Seasickness through Brain-Computer Interface-based Attention Shift
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913954463219712 |
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| author | Bao, Xiaoyu Xu, Kailin Zhu, Jiawei Huang, Haiyun Li, Kangning Huang, Qiyun Li, Yuanqing |
| author_facet | Bao, Xiaoyu Xu, Kailin Zhu, Jiawei Huang, Haiyun Li, Kangning Huang, Qiyun Li, Yuanqing |
| contents | Seasickness poses a widespread problem that adversely impacts both passenger comfort and the operational efficiency of maritime crews. Although attention shift has been proposed as a potential method to alleviate symptoms of motion sickness, its efficacy remains to be rigorously validated, especially in maritime environments. In this study, we develop an AI-driven brain-computer interface (BCI) to realize sustained and practical attention shift by incorporating tasks such as breath counting. Forty-three participants completed a real-world nautical experiment consisting of a real-feedback session, a resting session, and a pseudo-feedback session. Notably, 81.39\% of the participants reported that the BCI intervention was effective. EEG analysis revealed that the proposed system can effectively regulate motion sickness EEG signatures, such as an decrease in total band power, along with an increase in theta relative power and a decrease in beta relative power. Furthermore, an indicator of attentional focus, the theta/beta ratio, exhibited a significant reduction during the real-feedback session, providing further evidence to support the effectiveness of the BCI in shifting attention. Collectively, this study presents a novel nonpharmacological, portable, and effective approach for seasickness intervention, which has the potential to open up a brand-new application domain for BCIs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Alleviating Seasickness through Brain-Computer Interface-based Attention Shift Bao, Xiaoyu Xu, Kailin Zhu, Jiawei Huang, Haiyun Li, Kangning Huang, Qiyun Li, Yuanqing Human-Computer Interaction Artificial Intelligence Signal Processing Quantitative Methods Seasickness poses a widespread problem that adversely impacts both passenger comfort and the operational efficiency of maritime crews. Although attention shift has been proposed as a potential method to alleviate symptoms of motion sickness, its efficacy remains to be rigorously validated, especially in maritime environments. In this study, we develop an AI-driven brain-computer interface (BCI) to realize sustained and practical attention shift by incorporating tasks such as breath counting. Forty-three participants completed a real-world nautical experiment consisting of a real-feedback session, a resting session, and a pseudo-feedback session. Notably, 81.39\% of the participants reported that the BCI intervention was effective. EEG analysis revealed that the proposed system can effectively regulate motion sickness EEG signatures, such as an decrease in total band power, along with an increase in theta relative power and a decrease in beta relative power. Furthermore, an indicator of attentional focus, the theta/beta ratio, exhibited a significant reduction during the real-feedback session, providing further evidence to support the effectiveness of the BCI in shifting attention. Collectively, this study presents a novel nonpharmacological, portable, and effective approach for seasickness intervention, which has the potential to open up a brand-new application domain for BCIs. |
| title | Alleviating Seasickness through Brain-Computer Interface-based Attention Shift |
| topic | Human-Computer Interaction Artificial Intelligence Signal Processing Quantitative Methods |
| url | https://arxiv.org/abs/2501.08518 |